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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "from scipy import stats\n",
    "import numpy as np\n",
    "# N(0,2)+N(0,10)\n",
    "data1 = list(stats.norm.rvs(loc=0, scale=2, size=70000))\n",
    "data2 = list(stats.norm.rvs(loc=0, scale=20, size=30000))\n",
    "data = np.array(data1+data2)\n",
    " \n",
    "# 利用fitter拟合数据样本的分布\n",
    "from fitter import Fitter\n",
    "# may take some time since by default, all distributions are tried\n",
    "# but you call manually provide a smaller set of distributions\n",
    "f = Fitter(data, distributions=['norm', 't', 'laplace', 'rayleigh'])\n",
    "f.fit()\n",
    "f.summary() #返回排序好的分布拟合质量（拟合效果从好到坏）,并绘制数据分布和Nbest分布\n",
    "f.df_errors #返回这些分布的拟合质量（均方根误差的和）\n",
    "f.fitted_param #返回拟合分布的参数\n",
    "f.fitted_pdf #使用最适合数据分布的分布参数生成的概率密度\n",
    "print(f.get_best(method='sumsquare_error')) #返回最佳拟合分布及其参数\n",
    "f.hist() #绘制组数=bins的标准化直方图\n",
    "f.plot_pdf(names=None, Nbest=3, lw=2) #绘制分布的概率密度函数\n",
    "print(f.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}